Predictive fraud analysis system for data transactions

ABSTRACT

A method for execution by a computing entity of a data transactional network includes generating a plurality of risk analysis responses regarding a transaction for fraud evaluation, where the transaction is between a first computing device and a second computing device regarding transactional subject matter. The method further includes performing a first level interpretation of the plurality of risk analysis responses to produce a first level fraud answer. The method further includes determining a confidence of the first level fraud answer compares unfavorably with a confidence threshold. The method further includes determining a second level interpretation of the plurality of risk analysis responses based on a level of the confidence of the first level fraud answer. The method further includes performing the second level interpretation of the plurality of risk analysis responses to produce a fraud evaluation answer regarding the transaction.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility patent applicationSer. No. 17/646,723, entitled “MODIFYING ARTIFICIAL INTELLIGENCE MODULESOF A FRAUD DETECTION COMPUTING SYSTEM,” filed Jan. 1, 2022, which is acontinuation of U.S. Utility application Ser. No. 16/523,750, entitled“PREDICTIVE FRAUD ANALYSIS SYSTEM FOR DATA TRANSACTIONS,” filed Jul. 26,2019, issued as U.S. Pat. No. 11,218,494 on Jan. 4, 2022, all of whichare hereby incorporated herein by reference in their entirety and madepart of the present U.S. Utility patent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

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BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to data communication systems and moreparticularly to predictive fraud analysis within such systems.

Description of Related Art

Data communication systems include user computers, service providerservers, data storage servers, and network infrastructure that allowsthe various components of a system to communicate data with each other.The service providers provide a wide variety of digital data services,including, but not limited to, data storage services, streaming videoservices, digital music purchases, other digital content purchases(e.g., video files, gift cards, etc.), digital assistant services (e.g.,dictation, time management, contact management, project management,etc.), etc.

In such systems, a user, via his/her computer, establishes an accountwith a service provider, via the service provider's server. Once anaccount is established, the user, via his/her computer, accesses theservice provider's server to obtain a digital data service. Typically,obtaining a digital data service is at a cost to the user, which is paidthrough a digital data compensation transaction (e.g., credit cardpayment, payment service payment, a gift card payment, etc.) or abalance is added to the user's account, which is digitally paid at alater time.

Fraud with respect to setting up an account, use of a valid account,and/or with payment information cost service providers a significantamount of revenue. On the other side, a fraudulent service provider,digital impersonation of a valid service provider, and/or fraudulentpayment processing costs end-users a significant amount of money.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a datatransactional network in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computingdevice in accordance with the present invention;

FIG. 3 is a schematic and functional block diagram of an embodiment of afraud detection computing system in accordance with the presentinvention;

FIG. 4 is a functional block diagram of an embodiment of a transactionand related data for processing by the fraud detection computing systemin accordance with the present invention;

FIG. 5 is a functional block diagram of an embodiment of transactionaldata and tools of the fraud detection computing system for generating afraud evaluation answer for a transaction in accordance with the presentinvention;

FIG. 6 is a logic diagram of an example of a method executed by a frauddetection computing system for generating a fraud evaluation answer fora transaction in accordance with the present invention;

FIG. 7 is a functional diagram of an example of a fraud evaluationanswer in accordance with the present invention;

FIG. 8 is a logic diagram of an example of a method executed by a frauddetection computing system for generating an initial fraud evaluationanswer for a transaction in accordance with the present invention;

FIGS. 9A-9H are functional block diagrams of an example of generating afraud evaluation answer for a transaction of by a fraud detectioncomputing system in accordance with the present invention;

FIG. 10 is a functional block diagram of another example of generatingan initial fraud evaluation answer for a transaction of by a frauddetection computing system in accordance with the present invention;

FIG. 11 is a logic diagram of a further example of a method executed bya fraud detection computing system for updating an initial fraudevaluation answer for a transaction in accordance with the presentinvention;

FIGS. 12A-12G are functional block diagrams of another example ofgenerating a fraud evaluation answer for a transaction of by a frauddetection computing system in accordance with the present invention;

FIG. 13 is a logic diagram of an example of a method executed by a frauddetection computing system for updating fraud evaluation tools inaccordance with the present invention; and

FIG. 14 is a functional diagram of an example of a transaction-matrix inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a datatransactional network 10 that includes user computing devices 12, adigital service provider computing system 14, a fraud detectioncomputing system 16, a user information database (info DB) 18, one ormore networks 20, a user data verification service provider computingsystem 22, and a user computing device verification service providercomputing system 24. Each of the user computing devices 12 has aconstruct similar to the computing device 25 of FIG. 2 and is associatedwith a user. As used herein, a user is a person, a group of people(e.g., user group), a business, and/or other entity (e.g., a trust, anagency, etc.).

The network 20 includes one more local area networks (LAN) and/or one ormore wide area networks (WAN). Each WAN and/or LAN of the network 20 isa public network and/or a private network. A LAN may be a wireless-LAN(e.g., Wi-Fi access point, Bluetooth, ZigBee, etc.) and/or a wirednetwork (e.g., Firewire, Ethernet, etc.). A WAN may be a wired and/orwireless WAN, such as the Internet, cellular telephone infrastructure,data switching network, and/or satellite communication infrastructure.

Each of the fraud detection computing system 16, the user dataverification service provider computing system 22, and the usercomputing device verification service provider computing system 24includes one or more computing devices, such as the computing device 25of FIG. 2. Each of the user data verification service provider computingsystem 22 and the user computing device verification service providercomputing system 24 may include database.

Within the data transactional network, a computing device generallyincludes a computing core and is any electronic device that cancommunicate data, process data, and/or store data. A further generalityof a computing device is that it includes a central processing unit(CPU), a memory system, user input/output interfaces, peripheral deviceinterfaces, and an interconnecting bus structure.

As specific examples, a computing device is a portable computing deviceand/or a fixed computing device. A portable computing device is a socialnetworking device, a gaming device, a cell phone, a smart phone, apersonal digital assistant, a digital music player, a digital videoplayer, a laptop computer, a handheld computer, a tablet, a video gamecontroller, and/or any other portable device that includes a computingcore. A fixed computing device is a personal computer (PC), a computerserver, a cable set-top box, a satellite receiver, a television set, aprinter, home entertainment equipment, a video game console, and/or anytype of home or office computing equipment that includes a computingcore.

As an overview of operation, the data transaction network 10 supportstransactions regarding digital services between computing devices of thenetwork 10. In general, a transaction is an exchange of digitalinformation where one party provides first digital information (e.g., arequest for something) to a second party in exchange for receivingsecond digital information (e.g., a response to the request) regarding adigital service.

A digital service is one of a variety of services. For example, adigital service is accessing a streaming video and/or music for a fee(e.g., per video, a monthly subscription fee, etc.), where the firstdigital information includes user account information and a request forthe vide and the second digital information includes the streamingvideo. As another example, a digital service is using on-line software(e.g., word processing, illustrating, presentations, etc.) for a fee. Asyet another example, a digital service is regarding an on-line sale of agift card, or the like. As a further example, a digital service isregarding an on-line purchase of a gift card, or the like. As a stillfurther example, a digital service is regarding use of a gift card, orcredit card, to purchase an item that will be physically shipped fromthe service provider to the user. As a still further example, a digitalservice is regarding storage and/or access of digital information withan on-line data storage service provider for a fee. As a still furtherexample, a digital service is regarding a feature of a social mediaplatform. Note that, as used herein, on-line means engaging at least aportion of the data transaction network to at least partially support adigital service.

In a more specific example of operation, a user 26, via its usercomputing device 12, initiates a transaction regarding a digitalservice. For example, the user computing device 12-1 initiatestransaction 34 with the digital service provider computing system 14regarding digital service 30. As another example, the user computingdevice 12-2 initiates transaction 36 with the digital service providercomputing system 14 regarding digital service 32. As yet anotherexample, the user computing device 12-1 initiates transaction 38 withthe user computing device 12-2 regarding digital service.

In the example of transaction 34 between the user computing device 12-1(for its user 26-1) and the digital service provider computing system 14regarding digital service 30, the digital service provider computingsystem 14 receives a request for the transaction 34. Prior to processingthe transaction 34, the digital service provider computing system 14evokes the fraud detection computing system 16 to render a decision asto the likelihood that the transaction includes an element of fraud(e.g., fraudulent account, account take over, fraudulent paymentinformation, etc.). In an embodiment, the fraud detection computingsystem 16 is integrated with, embedded in, and/or affiliated with thedigital service provider computing system 14.

The fraud detection computing system 16 utilizes a selective combinationof evaluation tools, fraud analysis tools, and/or swarm organizationaltools to produce a fraud analysis model to render a fraud evaluationanswer based on a wide variety of data. The evaluation tools, fraudanalysis tools, and swarm organization tools are artificial intelligent(AI) modules that each execute a particular function(s) as will bedescribed in greater detail with reference to one or more of theremaining figures.

The fraud detection computing system 16 executes the fraud analysismodel using data it collects from various sources to automaticallyrender a fraud evaluation answer. The answer is one of “low risk offraud” or accept; a decision of “high risk of fraud” or reject'; or adecision of further review or agent review. The data used by the frauddetection computing system 16 is from the user information database 18,the user data verification service provider computing system 22, theuser computing device verification service provider computing system 24,and/or other sources of information relevant to fraud detection for aparticular transaction, for a particular user, for a particular digitalservice, and/or for a particular digital service provider computingsystem.

After an answer is rendered, the fraud detection computing system 16collects data regarding the accuracy of its fraud evaluation answer. Ifthe answer was incorrect, the fraud detection computing system 16determines the nature of the inaccuracy and makes adjustments to tools,data sets, and/or creates new tools to address the inaccuracy. With sucha system and methodology, automated fraud detection can be substantiallyimproved. For instance, processing of fraudulent transactions can bereduced from 7-9% to less than 1% with negligible impact on speed ofprocessing the transactions.

As an implementation variant, the user verification service providercomputing system 22 is integrated with, embedded in, and/or affiliatedwith the digital service provider computing system 14. As an extensionof the implementation variant or as another implementation variant, theuser computing device verification service provider computing system 24is integrated with, embedded in, and/or affiliated with the digitalservice provider computing system 14.

FIG. 2 is a schematic block diagram of an embodiment of a computingdevice 25 that includes a computing core 52, one or more input devices54 (e.g., keypad, keyboard, touchscreen, voice to text, etc.), one ormore audio output devices 56 (e.g., speaker(s), headphone jack, etc.),one or more visual output devices 58 (e.g., video graphics display,touchscreen, etc.), one or more universal serial bus (USB) devices, oneor more networking devices (e.g., a wireless local area network (WLAN)device 84, a wired LAN device 86, a wireless wide area network (WWAN)device 88 (e.g., a cellular telephone transceiver, a wireless datanetwork transceiver, etc.), and/or a wired WAN device 90), one or morememory devices (e.g., a flash memory device 92, one or more hard drives94, one or more solid state (SS) memory devices 96, and/or cloud memory98), and one or more peripheral devices.

The computing core 52 includes a video graphics processing unit 60, oneor more processing modules 62, a memory controller 64, main memory 66(e.g., RAM), one or more input/output (I/O) device interface module 68,an input/output (I/O) interface 70, an input/output (I/O) controller 72,a peripheral interface 74, one or more USB interface modules 76, one ormore network interface modules 78, one or more memory interface modules80, and/or one or more peripheral device interface modules 82. Each ofthe interface modules 68, 76, 78, 80, and 82 includes a combination ofhardware (e.g., connectors, wiring, etc.) and operational instructionsstored on memory (e.g., driver software) that is executed by theprocessing module 62 and/or a processing circuit within the interfacemodule. Each of the interface modules couples to one or more componentsof the computing device 12-16. For example, one of the IO deviceinterface modules 68 couples to an audio output device 56. As anotherexample, one of the memory interface modules 80 couples to flash memory92 and another one of the memory interface modules 80 couples to cloudmemory 98 (e.g., an on-line storage system and/or on-line backupsystem).

Note that a computing device of the digital service provider computingsystem 14, the fraud detection computing system 16, of the user dataverification service provider computing system 22, and/or of the usercomputing device verification service provider computing system 24 mayinclude more or less components than shown. For example, when acomputing device is functioning as a server, it may not include speakersand/or other IO components that are geared toward human interface. Asanother example, a computing device includes multiple processing modules62 and/or multiple main memories 66. As yet another example, a computingdevice includes only one network card coupled to the network interfacemodule 78.

FIG. 3 is a schematic and functional block diagram of an embodiment of afraud detection computing system 16. In functional terms, the frauddetection computing system 16 includes communication modules 100-104, apool of evaluation tools 106, a pool of risk assessment tools 108, and apool of swarm operation tools 110 operating on one or more of thecomputing devices of the system 16. The tools are artificial intelligent(AI) modules that each execute a particular function(s) to facilitatethe automatic generation of a fraud evaluation answer 118. In somecircles, the AI modules may be referred to as “computer bots” or “bots”.

The communication modules 100-104 are specific-function swarmoperational tools that include specific (e.g., proprietary, limitedaccess, etc.) application programming interfaces (API) to enablecommunication between the tools of the pool of evaluation tools 106, ofthe pool of risk assessment tools 108, and/or of the pool of swarmoperation tools 110 to support the fraud analysis model and/ormodifications to the model. The communication modules 100-104 furtherfunction to enable tools of the fraud analysis model to communicate witha pool of data sources 120 that is external to the fraud detectioncomputing system 16. The communication modules 100-104 further supporttools communicating with the user information database 18. Note that thepool of data sources 120 includes the user data verification serviceprovider computing system 22, the user computing device verificationprovider computing system 24, other sources of information relevant tofraud detection for a particular transaction, for a particular user, fora particular digital service, and/or for a particular digital serviceprovider computing system.

In an example of operation, the fraud detection computing system 16receives transaction data 122 regarding transactions. The transactiondata includes data regarding the transaction, which includes sourceidentity information regarding a source of the transaction, destinationidentity information regarding a destination of the transaction, andinformation regarding the digital service of the transaction action. Forexample, if the source of the transaction is a user, then the sourceidentity information includes the identity of the user and identity ofthe user's computing device; if the destination is a digital serviceprovider computing system 14, then the destination identity informationincludes identity of the service provider and identity of one or morecomputing devices of the system 14.

The fraud detection computing system 16 also receives, or has stored,system fraud tolerances 132 for the digital service provider computingsystem 14 and/or for the particular digital service being requested. Ingeneral, the system fraud tolerances 132 provide guidance for the frauddetection computing system 16 on how tight to apply its analysis (e.g.,the level of confidence an answer is right, before rendering it). Inmany instances, the system fraud tolerances 132 are a balancing ofcustomer experience, speed of service, and closing a sale of a digitalservice versus the risk of the sale being fraudulent (e.g., to a badactor, a bad actor impersonating a good actor, fraudulent payment,etc.). Each time the fraud detection computing system 16 renders adecision of “agent review”, the customer experience is degraded and thelikelihood that the sale won't close increases dramatically.

Further, each time an automated answer to accept or reject is wrong, itharms the service provider. As such, some service providers (e.g.,provides that have relatively low volume and relative high costs fordigital services) will want a tight tolerance to ensure that littlefraud actually occurs at the potential cost of more “agent review”answers. While other service providers (e.g., provides that haverelatively high volume and relative low costs for digital services) willhave a high tolerance to fraud transaction answers being wrong so theycan reduce the number of agent reviews.

For a given transaction and in accordance with the system fraudtolerances 132, the fraud detection computing system 16 creates a fraudevaluation model, which includes selected tools of the pools of tools106-108. The fraud detection computing system 16 then retrieves datafrom the pool of data sources 120, the user information database 18,and/or data sources. Applying the data to the fraud evaluation model,the fraud detection computing system 16 renders a fraud evaluationanswer 118, which may be a low risk answer 112 (e.g., accept thetransaction), a high-risk answer 114 (e.g., reject the transaction), oragent review answer 116.

When the answer is “agent review” 116, the transaction and the relevantdata are placed in a transaction review queue 126 for subsequent reviewby a person operating a reviewer computing device 128. At some point intime, the person retrieves, via the reviewer computing device 128, thetransaction and the relevant data from the queue 126 for human analysis.After human analysis, the person enters an agent's answer 132 (e.g.,accept or reject the transaction) into the reviewer computing device128.

The agent's answer 132 is provided to the fraud detection computingsystem 16 as feedback data 124. The fraud detection computing system 16utilizes the agent's answers 132 as well as charge back information andagents' decision probability reports to determine which automated fraudevaluation answers were wrong and why. From this analysis, the frauddetection computing system 16 updates existing tools and/or creates newtools to improve accuracy of the automated fraud evaluation answers.

FIG. 4 is a functional block diagram of an embodiment of a transactionand related data for processing by the fraud detection computing system16. The source 140 initiates the transaction 144 by sending a requestfor a digital service to the destination 142. The source 140 includes auser, the user's computing device, or devices, and the user's networkaffiliation, or affiliations. The destination 142 similarly includes auser, the user's computing device, or devices, and the user's networkaffiliation, or affiliations. Recall that a user is a person, a group ofpeople (e.g., user group), a business, and/or other entity (e.g., atrust, an agency, etc.).

To begin the fraud evaluation analysis, the fraud detection computingsystem 16 gathers information regarding the source 140, the destination142, and the transaction 144 from the pool of data sources 120, the userinformation database 18, and/or other sources. For the transaction, thefraud detection computing system 16 gathers transaction data, whichincludes one or more of, but not limited to:

-   subject matter information (e.g., information regarding the digital    service being requested such as name, type, purchase price, quantity    ordered, type of subject matter, value of subject matter, typical    nature of transaction regarding the subject matter, is source user    the type of user to engage in a transaction regarding the subject    matter, is destination user the type of user to engage in a    transaction regarding the subject matter, etc.);-   transmission medium information (e.g., information regarding how the    destination received the request for the digital service such as via    the internet, via a wireless data cellular service, via a Wi-Fi    connection, source location, destination location, etc.);-   transmission mannerism information (e.g., information regarding how    conventional the transmission medium information to provide insight    into “was the receiving mechanism typical for the request, for the    source, for the destination”, “is the source location typical”,    etc.);-   host layer information (e.g., per the OSI model, information to    provide insight into “was there anything unusual about the transport    layer, the session layer, the presentation layer, and/or the    application layer”, “if so, what is it”, etc.); and-   proxy information (e.g., information to provide insight into “did    the source use a proxy IP (internet protocol) address, a proxy    server, and/or other proxy means”, “if so, what type and to what    extent”, “how hard does it appear that the source is trying to hide    its true IP address”, etc.).

For information regarding the source user, the fraud detection computingsystem gathers one or more of the follow:

-   -   user personal information (e.g., name, nickname, mailing        address, billing address, age, phone number(s), email        address(es), etc.);    -   user account information (e.g., account number, age of account,        user name, user payment data (e.g., credit card information,        payment service account information, etc.), etc.);    -   user transaction history (e.g., account use history, types of        transactions, frequency of transactions, types of digital        services, frequency of use of the digital services, fraud        history with other digital services, payment history, nature of        payments, how many years user has been using digital services,        etc.);    -   user computer habit information (e.g., beacon information        regarding the user's computer habit information. The habits        include one or more of key stroke pattern, key stroke pressure,        web page navigation trends, use of shift keys, use of shortcuts,        experience level with current web site, computer proficiency,        frequency of pasting information, frequency of typing out        information, frequency and/or use of auto-fill information,        typing speed, frequency of type-o's, manner in which type-o's        are corrected (fix immediately, wait, delete word and start        over, edit word, etc.), etc.);

For information regarding each of the source's computing devices, thefraud detection computing system 16 gathers one of more of thefollowing:

-   -   device identification (e.g., device serial number, device age,        device service record, operating system(s), browser        applications, number and types of CPUs, memory capabilities,        device use-sleep patterns (e.g.., typically in sleep mode from 8        PM to 6 AM), CPU loading patterns (e.g., typically run light,        run heavy, varies), etc.);    -   device type (e.g., server, computer, laptop, cell phone, tablet,        game console, has the device been modified (e.g., indicates a        more sophisticated user), is it a stock device, etc.); and    -   user-device affiliation (e.g., device registration information,        account registration information (e.g., was this the device used        to set up the account, edit the account, etc.), purchase        information (e.g., amount, date, location, etc.), public Wi-Fi        use, etc.).

For each network affiliation per computing device of the user, the frauddetection computing system 16 gathers one or more of:

-   -   network identifier (e.g., network address, network name, etc.);    -   network type (e.g., LAN, WLAN, WAN, WWAN, cellular, internet,        etc.);    -   user-network affiliation information (e.g., service provider,        account name, user name, payment information, age of account,        data rate services, internet use tendencies, cell phone use        tendencies, type of service, etc.);    -   device-network affiliation information (e.g., device ID linked        to IP address, phone number, etc.); and    -   media layer information (e.g., physical layer, data link layer,        and network layer).

In addition to gathering information regarding the source, destination,and transaction, the fraud detection computing system 16 gathers serviceprovider information that includes one or more of, but is not limitedto:

-   -   bad actor traits and tendencies (e.g., flooding a system with        requests, fake IP addresses, etc.);    -   bad actor tools (e.g., many hacker tools leave a small digital        trace);    -   bad actor history (e.g., history of fraud attacks on system,        current level of fraud of system, etc.);    -   types of fraud (e.g., account take over, false user information,        false payment information, stolen payment information, etc.);        and    -   system behavior anomalies (e.g., use patterns that are outside        of the norm, etc.).

FIG. 5 is a functional block diagram of an embodiment of transactionaldata and tools of the fraud detection computing system 16 for generatinga fraud evaluation answer for a transaction. In this example, the frauddetection computing system 16 processes the data from the data sources150 via an evaluation tool set (or pool of tools) 106, a risk assessmenttool set 108, and a swarm processing tool set 110 to generate the fraudevaluation answer 118. The data obtained from the data sources 150 is asdiscussed with reference to FIG. 4 and includes one or more of userdata, device data, network data, transaction data, bad actor data, fraudtype data, and system use data. Note that the delineation of tools intotool sets is for convenience of discussion and any tool may be in adifferent tool set or may be a stand-alone tool that is not part of atool set.

The tools (e.g., AI modules, or BOTs) of the evaluation tool setinclude:

-   -   A Core Identity of Individual (ID) AI module that determines who        the buyer is, as opposed to who the buyer claims to be. Who is        this, actually? Does the system recognize this buyer? Is this        the legitimate buyer they claim to be, or is this a bad player        to be tracked?    -   A Familiarity Detection (FD) AI module that determines whether        the buyer appear to be familiar with the marketplace? A “new”        user should not exhibit extreme familiarity.    -   A Detecting Risky Behavioral Patterns (RB) AI module that        interprets behavioral patterns as indicative of profitable        customers, while other behavioral patterns are correlated with        revenue loss, with any and everything in between.    -   A Device Recognition (DR) AI module that determines whether the        current buyer's device (web, mobile, etc.) is something the        system has seen before? With this buyer?    -   A Univariate Predictor Variable Computation (weight of        evidence—WoE) AI module that transforms categories, counts,        determines financial amounts, time-spans, etc. and produces        therefrom a calculable risk likelihood, which can be used by        other tools as inputs.    -   An IP-Proxy Piercing (IP) AI module that looks at whether the        apparent IP-address of the buyer is not necessarily its real        IP-address. The apparent IP-address can be feigned through the        use of relay-proxy. This AI module determines whether a proxy is        involved in the online interaction with the online buyer, or        not. And if a proxy is involved, its determines the geolocation        of the real IP-address.    -   A Rule Decay over Time (RD) AI module that relies on a mixture        of machine-learned math models and propositional logic (rules).        Some rules are valuable for a long time. That stable logic        migrates into the math models with future model updates. Other        logic outlives its usefulness and this AI module depreciates the        importance of those transient logic patterns over time.    -   A Detection and Recognition of Emergent Entity (ER) AI module        that detects new phenomena, both good and bad, emerge over time        (from time to time). This AI module is an acronym for Entity        Recognition and detects the appearance of newly emergent        phenomena and forms formal entities around them.    -   A system and method for Multi-Model Fusion (MMF) AI module that        integrates multiple opinions, based on historical accuracy in        context. The risk assessment tools are differentially accurate        under a variety of circumstances termed context. This AI module        compares different opinions from different risk assessment tools        and determines how much credibility should be given to those        various opinions, based on current context, and then resolves to        a single opinion based on fusion mathematics.    -   A Making the Optimal Decision (DS) AI module that attempts to        make the best decision between the range of decision choices        available, given a system's accurate profiles & transaction        (TXN) risk score, with differential bias in favor of the goals        of the enterprise.    -   An Interfacing with external service providers (AS) AI module        that brokers and integrates with outside data source service        providers, flexibly and dynamically. This AI module includes a        single generic launcher, controller, and interface for        simplifying the management of multiple service providers.

The tools (e.g., AI modules, or BOTs) of the risk assessment tool setinclude:

-   -   A Recognizing Account Takeover (ATO-RB) AI module that estimates        the likelihood that the current buyer is not the legitimate        account holder they claim to be.    -   A Detecting Fake Account Registration (REG-RB) AI module that        estimates the likelihood that the current registration attempt        in progress is creating a fake account.    -   An Advanced AI module that detects the presence or likely        presence of extremely skilled Fraud criminals. Highly skilled        bad actors are often well-organized individuals or gangs or        groups with access to the best deception technology available        from the Dark Web marketplaces.    -   A Collusion (Buyer-Seller & Multi-Buyer) (COL) AI module that        estimates the likelihood that the buyer is fake, and is        colluding with possibly one or more other fake or real buyers        and/or fake secondary market sellers as confederates.    -   A Detecting and Evaluating Anomalous Behavioral Patterns (ZD) AI        module, where ZD is an acronym for zero-day exploit., a new        exploit or attack vector that has never been seen before today.        Machine-learned models rely on actuarial or historical data.        Such systems are vulnerable to ZD exploits because, by        definition, examples do not exist in the historical data. This        AI module compares dynamically observed patterns to “normal”        patterns. Within a degree of tolerance, misfits are determined        to be anomalous. Some anomalies, good anomalies are due to new        legitimate behavioral trends in the good customer population.        Those are good to know about. Some anomalies are operational,        due to some change in hardware or software that is having        unexpected side-effects. Those are also good to know about. And        some anomalies, bad anomalies, are due to innovative changes on        the part of fraud buyers in their ongoing attempt to defeat        fraud defenses. Those are really good to know about if one wants        to stop or minimize fraud loss sooner rather than later. This AI        module alerts on significant change, and classifies the change        in terms of the three outcomes just mentioned.    -   A Detecting and Evaluating Risky Behavioral Patterns (RB) AI        module that detects behavioral patterns that are correlated with        risk of TXN fraud such as Web Site Traversal Patterns (Path        Analytic, Temporal Analytic) or Gift-Card Selection Behavior,        etc.    -   A Detecting Fraudulent Login Attempts (LOGIN) AI Module that        estimates the likelihood that the buyer is faking credentials,        attempting to fraudulently login as a legitimate account holder,        by using stolen email and password information (credential        stuffing).    -   A Detecting and Recognizing Signs of Hacker-Tool use (HTR) AI        module, where HTR is an acronym for Hacker Tool Recognition.        This AI module estimates the likelihood that the buy attempt is        being controlled by, or that the attempt was wholly or partially        crafted with the help of Hacker Tools available on the Dark Web        marketplace. Hacker Tools all leave a fingerprint, like the        striations on a spent bullet from a gun barrel.

The tools (e.g., AI modules, or BOTs) of the swarm processing tool set,perform the administrative functions of the system 16, include:

-   -   A Squad Organization AI module that provides for static        structure and allows for or constrains dynamic structure        emergence to keep a team organized and effective.    -   A Communication AI module that enable Bot2Bot communication,        BotNet2Outside communication, and communication within the        system 16.    -   A Guidance & Control AI module to guide, assist, and/or control        many of the valuable abilities of an emerging swarming        community. The variable abilities are not declaratively        pre-specified and thus, at times, the model created by the        system 16 needs a degree of guidance and control in order to        align with domain-specific goals. This AI module ensures that        the resulting outcomes align with policy, initiatives, and needs        (i.e., the system fraud tolerances).    -   A Conflict Resolution (B2B, B2Self, B2C) AI module that resolves        what is the best path forward when two or more trusted        subsystems (e.g., a group of AI modules) are in conflict. This        AI module also resolves conflict between two or more AI modules,        between AI modules and Control AI modules, and also resolves        internal logic inconsistencies, contradictory within a single AI        module.    -   A Situation Awareness AI module that, at any given moment, the        system 16 needs to know what's going on in its operational        environment. In an adversarial domain, it's importance to know,        for instance, that the system is under attack. This AI module        dispels the fog of war through accurate situation assessment        (situation awareness).    -   A Swarming AI module, where swarming is an emergent behavior        that needs to not only converge but to converge on an optimal        solution. This AI module monitors the results of dynamic        swarming to determine the value of the swarmed solutions, and        enhance accordingly.    -   A Data Foraging AI module that, when the best direction for        optimal performance is not entirely obvious, this AI module        engages in exploratory foraging behavior to find new paths to        success.    -   A Self-Awareness AI module, where AI modules need to have a        clear understanding of their own state, their own state changes,        their own dynamics, and their own performance.    -   This AI module helps other AI modules that make up the        cooperative community, to understand themselves in such a way        that they can change their performance for the better.

The organization AI module processes the data from the data sources toprovide evidence vectors for the AI modules (e.g., tools) of the othertool sets. The organization module creates a unique evidence vector foreach tool that generates a score. For example, the organization modulecreates a unique evidence vector for each of at least some of:

-   -   The Core Identity of Individual (ID) AI module;    -   The Familiarity Detection (FD) AI module;    -   The Detecting Risky Behavioral Patterns (RB) AI module;    -   The Device Recognition (DR) AI module;    -   The Univariate Predictor Variable Computation (weight of        evidence—WoE) AI module;    -   The IP-Proxy Piercing (IP) AI module;    -   The Rule Decay over Time (RD) AI module;    -   The Detection and Recognition of Emergent Entity (ER) AI module;    -   The Recognizing Account Takeover (ATO-RB) AI module;    -   The Detecting Fake Account Registration (REG-RB) AI module;    -   The Advanced AI module;    -   The Collusion AI module;    -   The Detecting and Evaluating Anomalous Behavioral Patterns (ZD)        AI module;    -   The Detecting and Evaluating Risky Behavioral Patterns (RB) AI        module;    -   The Detecting Fraudulent Login Attempts (LOGIN) AI Module; and    -   The Detecting and Recognizing Signs of Hacker-Tool use (HTR) AI        module.

As a specific example, the organization module generates an evidencevector 125 for the core identity AI module. This evidence vectorincludes information that enables the core identity AI module todetermine who the buyer is, as opposed to who the buyer claims to be.Who is this, actually? Does the system recognize this buyer? Is this thelegitimate buyer they claim to be, or is this a bad player to betracked? As such, evidence vector includes user information, accountinformation, device information, user history information, and otherdata as the organization module deems important.

Each AI module that receives an evidence vector generates a score 129 or131 therefrom. In an example, the scores range from −1 to +1, where −1is representative of a high likelihood of fraud; +1 is representative ofa low likelihood of fraud; and 0 is representative of “don't know”. A“don't know” answer typically results when there is insufficient data toproduce a score and/or when a bad actor has manipulated data to create adon't know answer for one or more of the tools (e.g., bots).

The fraud detection computing system 16, typically via the organizationmodule, evaluations the scores from the other modules in a multi-layeredmanner. In a first layer, the organization module reviews the individualscores of the modules in light of the current transaction, where eachmodule is looking at the transaction from its individual perspective(e.g., legit-account abuse (promo abuse), legit-account takeover (ATO),ransomware, collusion, money-laundering, created-account-fraud,friendly-family fraud, login-evidence, registration-evidence).

As a specific example, when a module returns a score near “1”, themodule has a high degree of confidence that, from its perspective (e.g.,account take over), the transaction is not fraudulent. As anotherspecific example, when a module returns a score near “−1”, the modulehas a high degree of confidence that, from its perspective (e.g.,account abuse), the transaction is fraudulent. As yet another specificexample, when a module returns a score near “0”, the module has nodegree of confidence whether, from its perspective (e.g., create accountfraud), is fraudulent or not.

As a next level of review, the fraud detection computing system employsone or more of the organization module, the optimal decision module, theweight of evidence module, the organization module, and the multi-modemodule to generate an initial fraud evaluation answer. In the next levelof review, the module(s) interpret the scores from the other modules,including the score near zero, in light of previous transaction data toproduce an initial fraud evaluation score. For example, the scores forthe current transaction are evaluated in light of previous transactiondata of the transaction source, of the transaction destination, and/orof the transaction subject matter. The module(s) process the initialfraud evaluation score to produce an initial fraud evaluation answer of“accept”, “reject”, or “further review”. As a specific example, if theinitial fraud evaluation score is 0.75 and the fraud tolerance is 0.72,then the initial fraud evaluation answer accepts the transaction.

As another specific example, if the initial fraud evaluation score is−0.75 and the fraud tolerance 132 is −0.72, then the initial fraudevaluation answer is “reject the transaction”. As yet another specificexample, if the initial fraud evaluation score is greater than −0.72 andless than 0.72, then the initial fraud evaluation answer is “furtherreview”.

FIG. 6 is a logic diagram of an example of a method executed by acomputing entity of the fraud detection computing system for generatinga fraud evaluation answer for a transaction. As used herein, a computingentity includes one or more of a processing core of a computing device,a computing device, a plurality of computing device, and a plurality ofcloud-based processing resources (e.g., processing cores, memory,co-processing, etc.). The method begins at step 160 where the computingentity receives a transaction for fraud evaluation. The transaction isbetween a first computing device (e.g., a source device) of the datatransactional network and a second computing device (e.g., a destinationdevice) of the data transactional network regarding transactionalsubject matter (e.g., a digital service).

The method continues at step 162 where the computing entity sets up andexecutes an initial fraud evaluation model to produce an initial fraudassessment answer. The initial fraud assessment answer is low risk offraud (e.g., accept), high risk of fraud (e.g., reject), or furtheranalysis is required (e.g., further review). The setting up andexecuting the initial fraud evaluation module will be discussed ingreater detail with at least reference to FIG. 8. Note that fraudassessment answer and fraud evaluation answer mean substantially thesame thing.

The method continues at step 164 where the computing entity determineswhether the initial fraud assessment answer is “further analysis”. Whenthe initial fraud assessment answer is not further analysis, the methodcontinues to step 166, where the fraud detection computing systemoutputs the initial fraud assessment answer (e.g., accept or reject) asthe fraud evaluation answer.

When the initial fraud assessment answer is that further analysis isrequired, the method continues at step 168 where the computing entityexecutes a swarm process model to generate an updated fraud assessmentanswer. The execution of the swarm process model will be described ingreater detail with reference to at least FIG. 11. The method continuesat step 170 where the computing entity determines whether the updatedassessment answer is reliable.

The reliability of the updated assessment answer may be determined in avariety of ways. For example, the computing entity establishes aconfidence factor for the updated fraud assessment answer (e.g., a scoreof the fraud evaluation module, where the closer to 0 the answer is, theless confidence it is correct; the closer the score is to −1 or to +1,the higher the confidence factor). The computing entity then comparesthe confidence factor with a confidence threshold (which is based on thesystem fraud tolerances). When the confidence factor compares favorablywith the confidence threshold, the computing entity indicates that theupdated fraud assessment answer is reliable. When the updated fraudassessment answer is reliable, the method continues at step 166, wherethe answer is used.

When the updated assessment answer is unreliable, the method continuesat step 172 where the computing entity determines whether the updatedfraud assessment answer is a divergent answer. The computing entitydetermines whether the answer is divergent by interpreting answers oftools of the swarm process model to form a first group of answers thatfavor a low-risk answer and a second group of answers that favor ahigh-risk answer. When the two groups exist, indicating, by thecomputing entity, that the updated fraud assessment answer is adivergent answer. If only one group exists, then the answer is notdivergent.

When the updated assessment answer is unreliable and when the updatedfraud assessment answer is not a divergent answer, the method continuesto step 180 where the computing entity generates an answer of agentreview and queues the transaction for agent review. When the updatedfraud assessment answer is a divergent answer, the method continues atstep 174 where the computing entity executes a conflict resolution modelto generate a single answer at step 176.

In an embodiment, the executing of the conflict resolution modelincludes determining whether the swarm process model is optimal for thetransaction. This may be done in a variety of ways. For example, theswarm processing model is initially deemed to be optimal. As anotherexample, the swarm processing module is initially deemed to benon-optimal. As yet another example, the level of divergence in light ofthe system fraud tolerances is used to determine whether the swarmprocessing module is optimal or not.

When the swarm process model is optimal, the computing entity identifiesa first set of tools of the swarm process model that generated the firstgroup of answers and identifies a second set of tools of the swarmprocess model that generated the second group of answers. The computingentity then adjusts a weight factor of a fist set of weight factors(e.g., relating to integrity of input data, tool being used, integrityof response, use different data, use different parameters, etc.)associated with the first set of tools or of a second set of weightfactors associated with the second set of tools. The computing entitydetermines the type and amount of adjustment in light of the systemfraud tolerances and current system activities (e.g., percentage ofcurrent fraud activities) to produce an adjusted weight factor. Thecomputing entity then executes the swarm process model using theadjusted weight factor to generate the reliable fraud assessment answer.

The method continues at step 178 where the computing entity determineswhether the updated fraud assessment answer compares favorably to athreshold. When it does, the answer is used at step 166. When the answercompares unfavorably to the threshold, the method continues at step 180where the transaction is queued for agent review.

FIG. 7 is a functional diagram of an example of a fraud evaluationanswer that ranges from −1 to +1. The example further includesthresholds for a high risk of fraud answer and a low risk of fraudanswer. The thresholds are set based on the system fraud tolerances andmay be uneven. For example, a high threshold is set for rejecting atransaction based on the likelihood of it be fraudulent. As anotherexample, a lower threshold is use for accepting a transaction based onthe likelihood of it not being fraudulent. A score in the middle equatesto a further review answer. Note that any scale may be used for theanswer range.

FIG. 8 is a logic diagram of an example of a method executed by acomputing entity of a fraud detection computing system for generating aninitial fraud evaluation answer for a transaction, which corresponds tostep 162 of FIG. 6. This method begins at step 190 where the computingentity receives a transaction for fraud evaluation. The transaction isbetween a first computing device (e.g., a source) and a second computingdevice (e.g., a destination) regarding transactional subject matter(e.g., a digital service) that is transmitted via the data transactionnetwork. In addition, the computing entity receives data from datasources 120 and/or from the user information database 18.

The method continues at step 192 where the computing entity generatesevidence vectors regarding the transaction. As an example, an evidencevector is a piece of information regarding a topic, or portion of atopic, from a list of topics. The list includes:

-   user information regarding a user associated with the first    computing device;-   information regarding the first computing device;-   information regarding network affiliations of the user;-   anomaly information regarding one or more of the first computing    device, the second computing device, and the data transaction    network; and-   information regarding network affiliations of the first computing    device.

As another example, a second evidence vector is a second piece ofinformation regarding one of:

-   second user information regarding a second user associated with the    second computing device;-   information regarding the second computing device;-   information regarding network affiliations of the second user; and-   information regarding network affiliations of the second computing    device.

The method continues at step 194 where the computing entity engagestools (e.g., AI modules) to generate risk analysis responses based onthe evidence vectors. The tools are selected from a variety of sets oftools that include a set of risk assessment tools, a set of evidentiarytools, and a set of swarm processing tools.

The method continues at step 196 where the computing entity performs afirst level interpretation of the risk analysis responses from the poolof tools to produce a first level answer. The method continues at step198 where the computing entity determines whether a second levelinterpretation is needed. For example, when the individual scores of thetools all have a high confidence factor (e.g., compare favorably to thesystem fraud tolerances), a second level interpretation is not needed,but still may be performed to add to the confidence of the analysis. Asanother example, when the individual scores include indeterminate scores(e.g., near zero), include scores that don't compare favorably to thesystem fraud tolerances, and/or are conflicting (e.g., one score has ahigh confidence factor of fraud and a second score has a high confidencefactor of non-fraud), then the second level interpretation is needed. Ifthe second level interpretation is not needed, then the answer isoutputted at step 166.

When the second level of interpretation is needed, the method continuesat step 199 where the computing entity performs the second levelinterpretation. For example, the computing entity interpret the scores,including the score near zero, in light of previous transaction data toproduce an initial fraud evaluation score. For example, the scores forthe current transaction are evaluated in light of previous transactiondata of the transaction source, of the transaction destination, and/orof the transaction subject matter. The computing entity processes theinitial fraud evaluation score to produce an initial fraud evaluationanswer of “accept”, “reject”, or “further review”, which is outputted atstep 166.

FIGS. 9A-9H are functional block diagrams of an example of generating afraud evaluation answer for a transaction of by a fraud detectioncomputing system. FIG. 9A illustrates an organization module 200 (e.g.,a swarm processing tool) receiving user data and device data from datasources 150 and creating evidence vectors 202-208 therefrom. In thisexample, the user data includes user personal information, user accountinformation regarding one or more services provided via the datatransactional network, user transaction history, and/or user computerhabit information as previously defined. The device data includes deviceinformation, device type, and/or user-device affiliation information aspreviously defined. Note that the data gathered is per user and perdevice.

During an initial analysis, the fraud detection computing system 16engages some, if not all, of the evaluation tools and/or risk assessmenttools to obtain initial responses from the tools and create an initialfraud evaluation answer therefrom. In this example, the core identitymodule 210, the familiarity detection module 212, the risky behaviorpattern module 214, and the device recognition module 216 are engaged.Accordingly, the organization module 200 generates a user legitimacyevidence vector 202 for the core identity module 210; a user familiarityevidence vector 204 for the familiarity detection module 212; a userbehavior pattern evidence vector 206 for the risky behavior patternmodule 214, and a computing device legitimacy evidence vector 208 forthe device recognition module 216.

Each of the modules 210-216 process their corresponding evidence vectors202-208 to detect a hint of an abnormality that might be suggestive offraud (e.g., a bad actor impersonating a good actor or an actor doing abad thing). For example, the core identity module 210 determines a userlegitimacy score 218 as to whether there are any abnormalities with theuser's information and the manner which the user logged in. As anotherexample, the familiarity detection module 212 determines a userfamiliarity score 220 as to whether the exhibited navigation skills ofthe destinations website to initiate the transactions is commensuratewith the user's level of familiarity with the web site (e.g., a new usershould take more time to get to where he/she wants to go in comparisonto a more familiar user).

As yet another example, the risky behavior pattern module determines auser behavior pattern score 220 as to whether the exhibited interactionbetween the user and his/her computing device is different than expectedfor this user (e.g., different typing speed, different level oftype-o's, different use of cut and paste, etc.). As a further example,the device recognition module 216 determines a computing devicelegitimacy score 222 as to whether this is a device that has interactedwith the system before and that it has been typically affiliated with aknown valid user.

FIG. 9B illustrates the organization module 200 receiving network data,transaction data, and system use data from data sources 150 and creatingevidence vectors 242-252 therefrom. In this example, the network dataincludes (per user, per device, and/or per network) a networkidentifier, network type, user-network affiliation information,device-network affiliation information, and/or media layer informationas previously defined. The transaction data includes, per transaction,information regarding the transactional subject matter, transmissionmedium information regarding transmission of a request for thetransaction from the first computing device to the second computingdevice, host layer information, and/or proxy information as previouslydefined. The system use data includes fraud rate information (e.g.,historical and current) and information regarding other transactions ofthe system (e.g., both fraudulent and non-fraudulent transactions).

In this example, the core identity module 210-1, the IP module 232, thedevice recognition module 216-1, the risky behavior pattern module214-1, the emergent detection module 238, and the rule decay module 240and are engaged. Accordingly, the organization module 200 generates auser-network evidence vector 242 for the core identity module 210-1; anetwork access evidence vector 244 for the IP proxy module 232; acomputing device-network evidence vector 246 for the device recognitionmodule 216-1, a system interaction evidence vector 248 for the riskybehavior module 214-1, an anomaly evidence vector 250 for the emergentdetection module 238, and a system operation evidence vector 252 for therule decay module 240.

Each of the modules 210-1, 232, 216-1, 214-1, 238, and 240 process theircorresponding evidence vectors 242-252 to detect a hint of anabnormality that might be suggestive of fraud (e.g., a bad actorimpersonating a good actor or an actor doing a bad thing). For example,the core identity module 210-1 (e.g., a different module or an extensionof the core identity module 210) determines a user-network score 254 asto whether there are any abnormalities with the user's networkinformation and/or the manner in which the user used the network toaccess the destination of the transaction. As another example, the IPproxy module 212 determines a proxy score 256 and/or a location score asto whether the user, via its computing device, used an IP proxy and, ifso, determines the true location of the user's computing device.

As yet another example, the device recognition module 216-1 (e.g., adifferent module or an extension of the device recognition module 216)determines a computing device-network score 260 as to whether this is adevice-network interaction and/or network access to the system isconsistent with prior device-network interactions and/or network accessto the system for the user and its computing device. As a furtherexample, the risky behavior pattern module 214-1 (e.g., an extension ofmodule 214 or a different module) determines a system engagement score262 as to whether the exhibited interaction between the user and thesystem is different than expected for this user (e.g., transactionsubject matter, different quantities for an order, different pricepoints, etc.).

As a still further example, the emergent detection module 238 determinesan anomaly score 264 as to whether deviations from normal use of thesystem are indicative of a fraudulent attack. As an even furtherexample, the rule decay module 240 determines a system rule score 266,per rule or for a group of rules, regarding the decaying value,accuracy, and/or usefulness of the rule.

FIG. 9C illustrates the organization module 200 receiving bad actor dataand fraud type data from data sources 150 and creating evidence vectors270 and 272 therefrom. In this example, the bad actor data includes badactors' historical transaction data, legitimate users' transactionaldata, bad actor techniques, bad actor traits, and hacker tool remnants,as previously defined. The fraud type data includes data regardingaccount take over, fake account information, fraudulent login, fraudattempt rate, and/or multiple user collusion, which can be ascertainedfrom device data and/or user data.

In this example, the bad actor evaluation module 273 and the fraud ratemodule 275 and are engaged. Accordingly, the organization module 200generates a bad actor evidence vector 270 for the bad actor evaluationmodule 273 and a fraud rate evidence vector 252 for the rule decaymodule 240.

The bad actor evaluation module 273 determines a bad actor score 274 asto whether there are indications that the user may not be the actualuser. As another example, the fraud rate module 275 determines a fraudrate score 256 as to rate of fraud currently active in the system, rateof fraud in the past, rate of fraud surrounding this source, rate offraud surrounding this destination, rate of fraud regarding the digitalservice of the transaction.

FIG. 9D illustrates the risk assessment tools 108 generating risk scores280-294 from the evaluation scores 218-222, 254-266, 274, and 276. Forexample, the account take over risk module interprets one or more of theuser legitimacy score 218, the user familiarity score 220, the userbehavior pattern score 220, the computing device legitimacy score 222,the user-network score 254, the computing device-network score 260, andthe system rule score 266 to render an account take over score 280. Asanother example, the hacker tool risk module interprets one or more ofthe user legitimacy score 218, the user familiarity score 220, the userbehavior pattern score 220, the computing device legitimacy score 222,the user-network score 254, the proxy score 256, the location score 258,the computing device-network score 260, and the system rule score 266 torender a hacker tools score 282.

As another example, the fake account risk module interprets one or moreof the user legitimacy score 218, the user familiarity score 220, thecomputing device legitimacy score 222, the user-network score 254, theproxy score 256, the location score 258, and the system rule score 266to render a fake account registration score 284. As a further example,the fraudulent login risk module interprets one or more of the userlegitimacy score 218, the user familiarity score 220, the computingdevice legitimacy score 222, the user-network score 254, the proxy score256, the location score 258, the computing device-network score 260, andthe system rule score 266 to render a fraudulent login score 286.

As a still further example, the professional bad actor risk moduleinterprets one or more of the user legitimacy score 218, the userfamiliarity score 220, the user behavior pattern score 220, thecomputing device legitimacy score 222, the user-network score 254, theproxy score 256, the location score 258, the computing device-networkscore 260, the system engagement score 262, the anomaly score 264, andthe system rule score 266 to render a professional bad actor score 288.

As an even further example, the anomaly attack risk module interpretsone or more of the system engagement score 262, the anomaly score 264,and the system rule score 266 to render an anomaly attack score 290.

As yet another example, the behavior pattern risk module interprets oneor more of the user familiarity score 220, the user behavior patternscore 220, the computing device legitimacy score 222, the proxy score256, the location score 258, the system engagement score 262, theanomaly score 264, and the system rule score 266 to render a behaviorattack score 292. As yet another example, the collusion risk moduleinterprets one or more of the user legitimacy score 218, the userfamiliarity score 220, the user behavior pattern score 220, thecomputing device legitimacy score 222, the user-network score 254, theproxy score 256, the location score 258, the computing device-networkscore 260, the system engagement score 262, the anomaly score 264, andthe system rule score 266 to render a collusion score 294. Themulti-module fusion interprets the risk scores 280-294 to render aninitial fraud evaluation answer 296.

FIG. 9E illustrates an example of processing an evaluation score and/ora risk score. In this example, the score ranges from −1 to +1, where −1is indicative of a very high risk of fraud and a +1 is indicative of avery low risk of fraud. A score near 0 is indicative of “don't know”answer due to insufficient data, no data, or potentially fraudulent datato evaluation. For the scores that are near zero, they are used in thesecond level interpretation of the scores and may or may not be used inthe first level interpretation of the scores.

FIG. 9F illustrates an example of scores being grouped in two areas:some around 0 and some around +1. The scores around zero, are used inthe second level interpretation and the scores around +1 are used inboth the first and second levels to render the initial fraud analysisanswer. In this example, the answer would be low risk of fraud, oraccept the transaction assuming the scores near zero are based on nodata or insufficient data and not based on fraudulent data.

FIG. 9G illustrates an example of scores being grouped in two areas:some around 0 and some around −1. The scores around zero, are used inthe second level interpretation and the scores around +1 are used inboth the first and second levels to render the initial fraud analysisanswer. In this example, the answer would be high risk of fraud, orreject the transaction regardless of whether the scores near zero arebased on no data, insufficient data, or fraudulent data.

FIG. 9H illustrates an example of scores being grouped in four areas:some around zero, some in the low negative range, some in the lowpositive range, and some in the higher positive range but less than +1.The scores around zero, are used in the second level interpretation andthe remaining scores are used in both levels to render the initial fraudanalysis answer. In this example, there is no clear indication of fraudand no clear indication that there is no fraud. As such, the answerwould be further analysis is required.

FIG. 10 is a functional block diagram of another example of generatingan initial fraud evaluation answer for a transaction of by a frauddetection computing 16. In this example, the evidence vectors areprovided directly to the risk assessment tools 108. In comparison withFIGS. 9A-9H, the evaluation tools are skipped, their functionality isintegrated into the risk assessment tools, or their functionality isimplemented by the organization module and accounted for in the evidencevectors.

FIG. 11 is a logic diagram of a further example of a method executed bya computing entity of a fraud detection computing system for updating aninitial fraud evaluation answer, which corresponds to step 168 of FIG.6. The method begins at step 164 where the computing entity determinesthat an initial fraud evaluation model did not produce a reliable fraudevaluation answer as evidenced by the initial answer being “furtheranalysis”.

The method continues at step 300 wherein the computing entity adjusts anaspect of risk assessment data, the evaluation data, and/or of theinitial fraud evaluation model. This may be done in a variety of ways.For example, the parameters of an evaluation tool or a risk assessmenttool are adjusted to “loosen” or “tighten” its respective functionality,where loosen refers to being less stringent in analysis and tightenrefers to being more stringent in analysis. As another example, data isadded to or subtracted from an evidence vector. As a further example,scores from some evaluation tools as used as additional inputs to otherevaluation tools. As a still further example, weighting of scores ischanged. As an even further example, new tools are created and/or toolsare modified.

The method continues at step 302 where the computing entity adjusts theinitial fraud evaluation model based on the adjusted aspect of the riskassessment data to produce an updated fraud evaluation model. The methodcontinues at step 304 where the computing entity executes the updatedfraud evaluation model to produce an updated fraud evaluation answer anda corresponding confidence level.

The method continues at step 306 where the computing entity compares theconfidence level with a confidence threshold. If the confidence levelcompares favorably with the confidence threshold, the method continuesat step 166 where the answer is outputted. If, however, the confidencelevel compares unfavorably with the confidence threshold, the methodcontinues at step 172 of FIG. 6.

FIGS. 12A-12G are functional block diagrams of another example ofgenerating a fraud evaluation answer for a transaction by the frauddetection computing system. FIG. 12A illustrates an example ofgenerating an initial fraud analysis model based on the scores producedby the various modules. In this example, the modules that are includedin the initial fraud analysis model are in black outlined boxes withblack text and the modules that are being excluded from the initialfraud analysis model are in light grey outlined boxes with light greytext.

Thus, for this example, the initial fraud analysis model includes thecore identity module 210, the familiarity detection module 212, thedevice recognition module 216, the core identity module 210-1, thedevice recognition module 216-1, the bad actor evaluation module 273,and the fraud rate evaluation module 275 are used in the updated model.The evaluation tools that produced “don't care” scores and are beingused in the second level interpretation but are not used in the first.Such tools include the initial fraud analysis model are the riskybehavior pattern module 214, the IP proxy module 232, the risky behaviormodule 214-1, the emergent detection module 238, and the rule decaymodule 240.

FIG. 12B illustrates an example of the initial fraud analysis modelestablished by the fraud detection computing system 16. The initialmodel includes the core identity module 210, the familiarity detectionmodule 212, the device recognition module 216, the core identity module210-1, the device recognition module 216-1, the bad actor evaluationmodule 273, and the fraud rate evaluation module 275. The firstinterpretation level model further includes the account take over riskmodule, the hacker tool risk module, the fake account registration riskmodule, the fraudulent login risk module, the professional bad actorrisk module, and the collusion risk module. The multi-module fusion toolprocesses the risk scores 280-288 and 294 in accordance with the firstlevel interpretation. If a second level interpretation is needed, themulti-module fusion tool uses the scores of the modules (including thedon't know answer) shown in FIGS. 12A-12B to render the initial answer.

FIG. 12C illustrates an example of an updated fraud detection model,which the fraud detection computing system 16 created after the initialfraud detection model rendered an answer of “further review”. In thisexample, the risk and evaluation tools of FIG. 12B are augmented withmodules of the swarm processing tool set. For example, the situationawareness module generates data regarding the current system fraudthreat level 301 (e.g., a percentage of transactions are currentlysuspected to be fraudulent).

As another example, the information foraging module generates new modelpaths 303 based on finding new data and/or interpreting datadifferently. As a further example, the swarming evaluation for optimalconvergence module generates data regarding the value of swarmedsolutions 305 (e.g., how reliable are the tools converging to a singletrustworthy answer?). As a still further example, the self-awarenessmodule generates self-awareness information 307 for one or more of theevaluation tools and/or of the risk assessment tools. As an even furtherexample, the guidance and control module generates guidance and controldata 309 for one more tools of the evaluation tool set and/or of therisk assessment tool set based on the system fraud tolerances 132.

The data produced by the swarm processing tools affects one or moreother tools. For example, the various evaluation tools receive feedbackdata from one or more other evaluation tools to produce adjusted scores218-1 through 222-1, 254-1, 260-1, 274-1, and 276-1. The adjusted scoresare provided to the risk assessment tools, which produce adjusted riskscores 280-1 through 288-1 and 294-1. The multi-module fusion toolrenders an updated answer 296-1 based on the adjusted risk scores.

FIG. 12D1 further illustrates example scores produced by the fraudanalysis model of FIG. 12B. The scores are in the range of −1 to +1 asdiscussed with reference to FIG. 9E. For example, the user legitimacyscore, user familiarity score, the device-network score, the bad actorscore, and the fraud rate score are collectively indicative that thetransaction is not fraudulent. However, the device legitimacy score andthe user-network score are collectively indicative that the transactionis potentially fraudulent.

From the evaluation scores, the risk assessment tools generate theirrespective scores. For example, the account take over score issuggestive, but not dispositive, that the user's account has been takenover. The remaining risk assessment scores are suggestive that thetransaction is not fraudulent. Since the account take over score issuggestive of fraud and the remaining scores are not, the current answeris “further review”.

FIG. 12D2 shows the data evaluation scores in further review. As shown,the user legitimacy score is a 0.92 on a scale of −1 to +1. As such, thefraud detection computing system is fairly confident that the user isthe legitimate user. The user familiarity score is 0.68, which isindicative of the system 16 being fairly confident that the familiarityof the transaction is commensurate with the valid user's familiaritywith the system. The device legitimacy score is −0.41, which isindicative of the system 16 being somewhat confident that the computingdevice used for the transaction is not usual computing device used bythe valid user.

The user-network score is −0.36, which is indicative of the system 16being somewhat confident that the network access used for thistransaction is not the typical network access used by the valid user.The device-network score is 0.52, which is indicative of the system 16being fairly confident that, for the type of computing device, thenetwork work access is fairly typical. The bad actor score is 0.82,which is indicative of the system 16 being fairly confident that thetransaction does not involve a professional bad actor. The fraud ratescore is 0.77, which is indicative of the system 16 being fairlyconfident that the system is not currently experiencing a significantfraud attack.

From the current scores, there are two plausible answers for thedifference in answer. (1) The valid user has a new computing device thatit is using to access the system; or (2) An imposter has taken over thevalid user's account using the imposter' s computing device. The frauddetection computing system 16 makes changes to obtain a correct answerregarding the difference and factoring that into the final fraudevaluation answer.

FIG. 12E illustrates the fraud detection computing system 16 makingchanges to the fraud evaluation model. In this example, the system makesadjustments to substantially prove that the user's account has beentaken over. With this decision made, the system 16 adds the IP proxymodule to the model and provides wider parameters 314 and 316 to the IPproxy module 232 and to the bad actor module and provide narrowerparameters 310 and 312 to the core identity module 210 and thefamiliarity detection module 212. The wider parameters include more datato review, changing data evaluation filtering levels (e.g., letting moreor less through), and/or changing how the data is interpreted (e.g.,biased in one director or another).

Based on these parameter changes, the evaluation tools generate updatedscores. The risk assessment tools also generate updated data scores thatare fed to the multi-module fusion tool, which renders another updatedanswer 299-2. For this example, the updated answer is “high risk offraud” or reject the transaction.

FIG. 12F shows update data evaluation scores in further review. Asshown, the user legitimacy score dropped from 0.92 to 0.57, whichreduces the system's confidence that this is a valid user. The userfamiliarity score is now −0.25, which is indicative of the system 16being fairly confident that the familiarity of the transaction is notcommensurate with the valid user's familiarity with the system. Thedevice legitimacy score, the user-network score, and the device-networkscore remained the same.

The bad actor score dropped from 0.82 to 0.28, which is indicative ofthe system 16 is much less confident that the transaction does notinvolve a professional bad actor. The fraud rate score dropped from 0.77to 0.17, which is indicative of the system 16 being much less confidentthat the system is not currently experiencing a significant fraudattack. The proxy score is −0.77, which is indicative of a highprobability that a proxy address is being used. The location score is−0.66, which is indicative of a high probability that the user'slocation is not consistent with one of the valid user's typicallocations.

FIG. 12G shows the updated fraud scores. In this example, the updatedaccount take over score is −0.84 and the professional bad actor score is0.79. Given the strong possibility that the account has been taken overby a bad actor, the answer is updated to be “high risk of fraud” orreject the transaction.

FIG. 13 is a logic diagram of an example of a method executed by acomputing entity of a fraud detection computing system for updatingfraud evaluation and risk tools. The method begins at step 320 where thecomputing entity renders fraud evaluation answers regarding transactionsas previously discussed.

The method continues at step 322 where the computing entity generates atransaction-answer matrix regarding the transactions and thecorresponding answers. For example, the computing entity obtains chargeback reports (e.g., charges back to accounts for fraudulenttransactions, which may occur months after the transaction errantlyapproved). The computing entity further obtains probability reports(e.g., reports regarding agent answers and their correctness, which isgenerally received within days after the transaction occurred). Thecomputing entity then updates the transaction-answer matrix with datafrom the charge back reports and with data from the probability reports.An example of a transaction-answer matrix will be discussed in greaterdetail with reference to FIG. 14.

The method continues at step 324 where the computing entity selects anentry from the transaction-answer matrix for evaluation. The methodcontinues to step 326 where the computing entity determines whether theanswer rendered (e.g., automated by the system or the agent's answer) ofthe selected entry was correct. If it was correct, the method continuesat step 328 where the computing entity determines whether the matrix hasbeen exhausted (e.g., all the entries reviewed). If yes, the methodends. If not, the method repeats at step 324.

When the answer is incorrect, the method continues at step 330 where thecomputing entity reconstructs the fraud assessment model for thetransaction. The fraud assessment model includes a set of evaluationtools, a set of risk assessment tools, and a set of swarm processingtools. The method continues at step 332 where the computing entityobtains inputted data that was used by the fraud assessment model toproduce the corresponding fraud evaluation answer. The method continuesat step 334 where the computing entity obtains additional data regardingthe transaction. The additional data includes additional source dataregarding a source of the transaction, additional destination dataregarding a destination of the transaction, additional network dataregarding a network that supported the transaction, additional bad actordata, additional transaction data regarding the transaction, and/oradditional fraud type data.

The method continues at step 336 where the computing entity augments theinputted data with the additional data to produce updated data. Themethod continues at step 338 where the computing entity executes thefraud assessment model using the updated data to generate an updatedfraud evaluation answer. The method continues at step 340 where thecomputing entity determines whether the updated fraud evaluation answeris correct.

When the updated fraud evaluation answer compares favorably to theactual fraudulent or non-fraudulent indication, the method continues atstep 342 where the computing entity determines differences in theinputted data and the additional data to produce difference data. Themethod continues at step 344 where the computing entity adjusts a toolof the fraud assessment model based on the difference data.

When the updated fraud evaluation answer compares unfavorably to theactual fraudulent or non-fraudulent indication, the method continues atstep 346 where the computing entity determines differences in theinputted data and the additional data to produce difference data. Themethod continues at step 348 where the computing entity creates a newtool for inclusion in the fraud assessment model based on the differencedata.

FIG. 14 is a functional diagram of an example of a transaction-matrix350 that includes a plurality of entries. Each entry includes anidentifier field 352, an auto answer field 354, an agent answer field356, an actual fraud status field 358, and a prediction accuracy field360. The identifier field stores an identity of the transaction; theautomated answer field stores a corresponding fraud evaluation answer;the agent answer field stores an agent answer when the correspondingfraud evaluation answer is agent review; actual fraud status storeswhether transaction was actually fraudulent or not; and the predictedaccuracy field stores an indication as to whether the answer was corrector not.

In this example, the first entry has the auto answer of “accept”, whichturned out to be true (i.e., correct). The second entry has the autoanswer of “reject”, which turned out to be true (i.e., correct). Thethird entry has the auto answer of “accept”, which turned out to befalse (i.e., incorrect). The fourth entry has the auto answer of“reject”, which turned out to be false (i.e., incorrect). The fifthentry has the auto answer of “agent review” and an agent answer of“accept”, which turned out to be true (i.e., correct). The sixth entryhas the auto answer of “agent review” and an agent answer of “reject”,which turned out to be true (i.e., correct). The seventh entry has theauto answer of “agent review” and an agent answer of “accept”, whichturned out to be false (i.e., incorrect). The eighth entry has the autoanswer of “agent review” and an agent answer of “reject”, which turnedout to be false (i.e., incorrect).

From the transaction-answer matrix, the computing entity of the frauddetection computing system 16 would review the fraud determinationprocess for entries 3, 4, 7, and 8. Depending on the nature of why theanswer was wrong, the computing entity creates a new tool, or tools,and/or modifies one or more existing tools.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form asolid-state memory, a hard drive memory, cloud memory, thumb drive,server memory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing entity of adata transactional network comprises: generating a plurality of riskanalysis responses regarding a transaction for fraud evaluation, whereinthe transaction is between a first computing device of the datatransactional network and a second computing device of the datatransactional network regarding transactional subject matter; performinga first level interpretation of the plurality of risk analysis responsesto produce a first level fraud answer; determining a confidence of thefirst level fraud answer compares unfavorably with a confidencethreshold; determining a second level interpretation of the plurality ofrisk analysis responses based on a level of the confidence of the firstlevel fraud answer; and performing the second level interpretation ofthe plurality of risk analysis responses to produce a fraud evaluationanswer regarding the transaction.
 2. The method of claim 1, wherein thefraud evaluation answer is one of: further analysis is required; a lowrisk of fraud; and a high risk of fraud.
 3. The method of claim 1further comprises: generating a plurality of evidence vectors regardingthe transaction, wherein an evidence vector of the plurality of evidencevectors is a piece of information regarding a topic, or portion thereof,of a list of topics.
 4. The method of claim 3 further comprises:engaging a plurality of tools; and generating, by the plurality oftools, the plurality of risk analysis responses based on the pluralityof evidence vectors.
 5. The method of claim 4, wherein the plurality oftools comprises: a set of risk assessment tools.
 6. The method of claim4, wherein the plurality of tools comprises: a set of evidentiary tools.7. The method of claim 4, wherein the plurality of tools comprises: aset of swarm processing tools.
 8. The method of claim 3, wherein thetopic comprises user information regarding a user associated with thefirst computing device.
 9. The method of claim 8, wherein the topiccomprises information regarding network affiliations of the user. 10.The method of claim 3, wherein the topic comprises information regardingthe first computing device.
 11. The method of claim 10, wherein theinformation regarding the first computing device comprises one or moresub-topics of: device information; device type; and user-deviceaffiliation information.
 12. The method of claim 3, wherein the topiccomprises anomaly information regarding the first computing device. 13.The method of claim 12, wherein the anomaly information furthercomprises information regarding the second computing device.
 14. Themethod of claim 12, wherein the anomaly information further comprisesinformation regarding the data transaction network.
 15. The method ofclaim 3, wherein the topic comprises anomaly information regarding thedata transaction network.
 16. The method of claim 3, wherein the topiccomprises information regarding network affiliations of the firstcomputing device.
 17. The method of claim 3, wherein the list of topicsfurther includes: transaction mechanism information that includes one ormore sub-topics of: information regarding the transactional subjectmatter; transmission medium information regarding transmission of arequest for the transaction from the first computing device to thesecond computing device; host layer information; and proxy information.18. The method of claim 3, wherein the list of topics further includes:bad actor information that includes one or more sub-topics of: markersindicating use of hacker tools; and professional bad actor indicators.19. The method of claim 3, wherein the list of topics further includes:fraud information that includes one or more sub-topics of: account takeover; fake account information; fraudulent login; fraud attempt rate;and multiple user collusion.
 20. The method of claim 1, wherein theperforming the first level interpretation on the plurality of riskanalysis responses is further based on risk tolerance inputs associatedwith the second computing device.